kopia lustrzana https://github.com/Shawn-Shan/fawkes
67 wiersze
2.3 KiB
Python
67 wiersze
2.3 KiB
Python
import numpy as np
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from mtcnn import MTCNN
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def to_rgb(img):
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w, h = img.shape
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ret = np.empty((w, h, 3), dtype=np.uint8)
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ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img
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return ret
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def aligner():
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return MTCNN()
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def align(orig_img, aligner, margin=0.8, detect_multiple_faces=True):
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""" run MTCNN face detector """
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minsize = 20 # minimum size of face
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threshold = [0.6, 0.7, 0.7] # three steps's threshold
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factor = 0.709 # scale factor
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if orig_img.ndim < 2:
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return None
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if orig_img.ndim == 2:
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orig_img = to_rgb(orig_img)
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orig_img = orig_img[:, :, 0:3]
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bounding_boxes = aligner.detect_faces(orig_img)
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nrof_faces= len(bounding_boxes)
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if nrof_faces > 0:
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det = bounding_boxes[0]['box']
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det_arr = []
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img_size = np.asarray(orig_img.shape)[0:2]
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if nrof_faces > 1:
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margin = margin / 1.5
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if detect_multiple_faces:
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for i in range(nrof_faces):
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det_arr.append(np.squeeze(bounding_boxes[i]['box']))
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else:
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bounding_box_size = (det[1] + det[3])
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img_center = img_size / 2
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offsets = np.vstack([(det[0] + det[2]) / 2 - img_center[1],
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(det[1] + det[3]) / 2 - img_center[0]])
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offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
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index = np.argmax(bounding_box_size - offset_dist_squared * 2.0) # some extra weight on the centering
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det_arr.append(det[index, :])
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else:
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det_arr.append(np.squeeze(det))
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cropped_arr = []
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bounding_boxes_arr = []
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for i, det in enumerate(det_arr):
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det = np.squeeze(det)
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bb = np.zeros(4, dtype=np.int32)
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# add in margin
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marg1 = int((det[2] - det[0]) * margin)
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marg2 = int((det[3] - det[1]) * margin)
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bb[0] = max(det[0] - marg1/2, 0)
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bb[1] = max(det[1] - marg2/2, 0)
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bb[2] = min(det[0] + det[2] + marg1/2, img_size[0])
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bb[3] = min(det[1] + det[3] + marg2/2, img_size[1])
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cropped = orig_img[bb[0]:bb[2], bb[1]: bb[3],:]
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cropped_arr.append(cropped)
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bounding_boxes_arr.append([bb[0], bb[1], bb[2], bb[3]])
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return cropped_arr, bounding_boxes_arr
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else:
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return None |